Testing the TVI framework against known historical outcomes across four domains
We validate the TVI framework using known historical outcomes. Rather than predicting future persistence (which would require waiting years to verify), we test whether the formula correctly ranks entities whose relative durability we already know.
Key advantage: This separates formula validation from parameter estimation. If the formula's structure correctly captures temporal dynamics, it should produce correct rankings even with reasonable parameter estimates.
| Content | TVI | Known Outcome |
|---|---|---|
| Charlie Bit My Finger (2007) | 67.52 | Foundation - referenced 17+ years |
| Gangnam Style (2012) | 41.76 | Foundation - first to 1B views |
| Ice Bucket Challenge (2014) | 5.03 | Faded - rarely mentioned now |
| Damn Daniel (2016) | 0.01 | Ephemeral - forgotten |
| Random TikTok 2023 | 0.00 | Ephemeral - gone in days |
In this retrospective sample, the formula places foundations above ephemera in the expected order.
| Methodology | TVI-B | Known Outcome |
|---|---|---|
| SMART Goals (1981) | 1,031 | Universal standard - taught everywhere |
| Agile/Scrum (2001) | 316 | Foundation - industry standard in tech |
| Six Sigma (1986) | 224 | Established - still used in manufacturing |
| OKRs (1999/2013) | 29 | Growing - adopted by tech companies |
| Holacracy (2015) | 0.17 | Niche - failed to gain traction |
| Dataset | TDIS | Known Outcome |
|---|---|---|
| MNIST (1998) | 40.21 | Pedagogical standard - 26 years |
| ImageNet (2009) | 7.97 | Foundation - enabled deep learning |
| CIFAR-10 (2009) | 3.19 | Standard benchmark |
| LAION-5B (2022) | 0.00 | Uncertain - legal issues |
| Company | ISPS | Known Outcome |
|---|---|---|
| Apple | 7,775 | +57% in 2008, +82% in 2020 |
| Microsoft | 6,783 | Survived all major crises |
| Amazon | 6,774 | Survived dot-com, 2008, COVID |
| Peloton | 82 | -92% from peak in 2022 |
| Lehman Brothers | 33 | Collapsed 2008 despite 158 years |
| WeWork | 8 | Failed IPO, bankruptcy |
| Domain | Expected Ranking | Validated |
|---|---|---|
| Viral Content | Charlie > Gangnam > TikTok | ✓ Yes |
| Methodologies | SMART > Agile > Holacracy | ✓ Yes |
| AI Datasets | MNIST > ImageNet > LAION | ✓ Yes |
| Companies | Apple > Microsoft > WeWork | ✓ Yes |
Result: The framework demonstrated directional consistency across the evaluated retrospective datasets under the tested parameter ranges.
To test robustness, we varied all parameters by ±20% (125 combinations) for the MNIST vs. LAION comparison.
Result: MNIST ranked higher than LAION in 100% of variations.
The ranking remained stable under substantial parameter uncertainty in this test. This suggests the core structure can model temporal dynamics without relying on a single precise parameter calibration.